Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note3 min read

RepoProbe: Benchmarking Architecture-Aware Repository Comprehension with Checklists

Source

arXiv

arxiv.org/abs/2608.04783v1

Read the source

Source

Plain-Language Brief

The text discusses a new benchmark called RepoProbe for evaluating the ability of Large Language Models (LLMs) to understand repository-scale code. The benchmark uses open-ended questions from GitHub Discussions and focuses on architectural inquiries instead of defect reporting. A Checklist-Based Verification Protocol is proposed to verify the answers, replacing subjective ratings with objective verification. The evaluation shows that state-of-the-art LLMs often struggle with both clarity and technical correctness.

What the Document Says

  • There's a shift in focus from function-level generation to repository-scale assistance due to the integration of Large Language Models (LLMs) into software engineering.
  • Existing benchmarks rely heavily on GitHub Issues, which can lead to models bypassing genuine understanding via pattern matching on error logs.
  • Edit Bias is a problem where models prematurely propose code modifications instead of understanding the existing repository architecture.
  • Current LLM-as-a-Judge scalar scoring suffers from high variance and low interpretability.
  • RepoProbe is a new benchmark that uses open-ended Q&A through GitHub Discussions, focusing on architectural inquiries.
  • A Checklist-Based Verification Protocol decomposes answers into atomic, verifiable facts and replaces subjective ratings with objective verification.
  • Evaluation of state-of-the-art LLMs reveals a gap between high clarity and evidence-grounded technical correctness.
  • The evaluation quantitatively confirms the prevalence of edit bias, where models prioritize code generation over architectural analysis.
  • RepoProbe's verification protocol significantly improves evaluation reliability compared to traditional evaluations with scalar scoring.

Key Concepts Mentioned

  • Large Language Models (LLMs)
  • Repository-scale assistance
  • Edit Bias
  • GitHub Issues
  • Open-ended Q&A
  • GitHub Discussions
  • Checklist-Based Verification Protocol
  • Evaluation
  • State-of-the-art LLMs
  • Clarity
  • Technical correctness
  • Code generation
  • Architectural analysis

Claims Made by the Document

  • There's a shift in focus from function-level to repository-scale assistance.
  • Existing benchmarks rely heavily on GitHub Issues and can lead to models bypassing genuine understanding.
  • Edit Bias is a problem where models prioritize code generation instead of understanding architecture.
  • Current scalar scoring suffers from high variance and low interpretability.
  • RepoProbe's Q&A-based approach improves evaluation of repository-level code understanding.
  • The Checklist-Based Verification Protocol improves evaluation reliability.

Evidence Used

  • Integration of LLMs into software engineering
  • Pattern matching on error logs
  • High clarity and technical correctness gap in SOTA LLMs

Methods / Approach

  • Open-ended Q&A through GitHub Discussions
  • Checklist-Based Verification Protocol

Limitations Stated by the Authors

Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

The study only evaluates the performance of state-of-the-art LLMs. The benchmark may not be applicable to all types or sizes of repositories.

Open Questions

  • What are the specific types of repositories where RepoProbe might not be applicable?
  • How does the Checklist-Based Verification Protocol handle ambiguous answers?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text discusses a new benchmark for evaluating LLMs, but it doesn't specify what kind of LLMs are being referred to. It also mentions a gap between clarity and technical correctness in SOTA LLMs, but it's unclear what 'SOTA' stands for.

Raw Abstract / Excerpt

The integration of Large Language Models (LLMs) into software engineering has shifted the focus from function-level generation to repository-scale assistance. However, existing benchmarks largely rely on bug reports from GitHub Issues, which often allow models to bypass genuine understanding via pattern matching on error logs. This misalignment under-measures Edit Bias, which refers to premature generation, where models prematurely propose code modifications instead of understanding the existing repository architecture. Furthermore, current LLM-as-a-Judge scalar scoring suffers from high variance and low interpretability. This work introduces RepoProbe, a novel benchmark for evaluating repository-level code understanding through open-ended Q&A using GitHub Discussions, which focuses on open-ended architectural inquiries rather than defect reporting. To ensure rigorous evaluation, we propose a Checklist-Based Verification Protocol that decomposes answers into atomic, verifiable facts, thereby replacing subjective ratings with objective verification. Our evaluation of state-of-the-art (SOTA) LLMs reveals a persistent gap between high clarity and evidencegrounded technical correctness. It also quantitatively confirms the prevalence of edit bias, in which models prioritize code generation instead of architectural analysis. Finally, we demonstrate that our verification protocol significantly improves evaluation reliability compared to traditional evaluations with scalar scoring.